Overview

Dataset statistics

Number of variables22
Number of observations60
Missing cells543
Missing cells (%)41.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 KiB
Average record size in memory184.0 B

Variable types

Text2
Unsupported2
Categorical5
Numeric13

Alerts

Region has constant value ""Constant
Division has constant value ""Constant
Games_Level has constant value ""Constant
Qualifier has constant value ""Constant
Chad1000x (s) has constant value ""Constant
Back Squat (lbs) is highly overall correlated with Clean and Jerk (lbs) and 2 other fieldsHigh correlation
Clean and Jerk (lbs) is highly overall correlated with Back Squat (lbs) and 2 other fieldsHigh correlation
Deadlift (lbs) is highly overall correlated with Back Squat (lbs) and 2 other fieldsHigh correlation
Fight Gone Bad is highly overall correlated with Filthy 50 (s) and 4 other fieldsHigh correlation
Filthy 50 (s) is highly overall correlated with Fight Gone Bad and 6 other fieldsHigh correlation
Fran (s) is highly overall correlated with Helen (s) and 1 other fieldsHigh correlation
Grace (s) is highly overall correlated with Deadlift (lbs) and 1 other fieldsHigh correlation
Helen (s) is highly overall correlated with Fight Gone Bad and 6 other fieldsHigh correlation
Max Pull-ups is highly overall correlated with Filthy 50 (s) and 1 other fieldsHigh correlation
Rank is highly overall correlated with Fight Gone Bad and 2 other fieldsHigh correlation
Run 5k (s) is highly overall correlated with Fight Gone Bad and 3 other fieldsHigh correlation
Snatch (lbs) is highly overall correlated with Back Squat (lbs) and 1 other fieldsHigh correlation
Sprint 400m (s) is highly overall correlated with Fight Gone Bad and 4 other fieldsHigh correlation
Affiliate has 3 (5.0%) missing valuesMissing
Country has 60 (100.0%) missing valuesMissing
Back Squat (lbs) has 6 (10.0%) missing valuesMissing
Clean and Jerk (lbs) has 3 (5.0%) missing valuesMissing
Deadlift (lbs) has 5 (8.3%) missing valuesMissing
Snatch (lbs) has 4 (6.7%) missing valuesMissing
Fight Gone Bad has 52 (86.7%) missing valuesMissing
Max Pull-ups has 40 (66.7%) missing valuesMissing
Chad1000x (s) has 59 (98.3%) missing valuesMissing
L1 Benchmark (s) has 60 (100.0%) missing valuesMissing
Filthy 50 (s) has 54 (90.0%) missing valuesMissing
Fran (s) has 25 (41.7%) missing valuesMissing
Grace (s) has 31 (51.7%) missing valuesMissing
Helen (s) has 50 (83.3%) missing valuesMissing
Run 5k (s) has 45 (75.0%) missing valuesMissing
Sprint 400m (s) has 46 (76.7%) missing valuesMissing
Athlete has unique valuesUnique
Country is an unsupported type, check if it needs cleaning or further analysisUnsupported
L1 Benchmark (s) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-02-18 02:59:42.437094
Analysis finished2024-02-18 02:59:57.718151
Duration15.28 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Athlete
Text

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size960.0 B
2024-02-17T21:59:57.851867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length30
Median length24
Mean length14.833333
Min length10

Characters and Unicode

Total characters890
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)100.0%

Sample

1st rowKatelin Van Zyl
2nd rowTanha Bouffe
3rd rowMadeline Sturt
4th rowGemma Rader
5th rowLuiza Marques
ValueCountFrequency (%)
madeline 3
 
2.4%
stephanie 2
 
1.6%
allison 2
 
1.6%
samantha 2
 
1.6%
katelin 1
 
0.8%
fisher 1
 
0.8%
jacqueline 1
 
0.8%
dahlstrøm 1
 
0.8%
shelling 1
 
0.8%
lauren 1
 
0.8%
Other values (111) 111
88.1%
2024-02-17T21:59:58.192608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 93
 
10.4%
e 93
 
10.4%
l 70
 
7.9%
n 66
 
7.4%
66
 
7.4%
i 57
 
6.4%
r 53
 
6.0%
o 43
 
4.8%
s 41
 
4.6%
t 29
 
3.3%
Other values (41) 279
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 696
78.2%
Uppercase Letter 127
 
14.3%
Space Separator 66
 
7.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 93
13.4%
e 93
13.4%
l 70
10.1%
n 66
9.5%
i 57
8.2%
r 53
7.6%
o 43
 
6.2%
s 41
 
5.9%
t 29
 
4.2%
d 25
 
3.6%
Other values (17) 126
18.1%
Uppercase Letter
ValueCountFrequency (%)
S 20
15.7%
H 12
 
9.4%
M 12
 
9.4%
A 8
 
6.3%
C 8
 
6.3%
W 7
 
5.5%
L 7
 
5.5%
E 6
 
4.7%
R 6
 
4.7%
B 5
 
3.9%
Other values (12) 36
28.3%
Space Separator
ValueCountFrequency (%)
66
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 823
92.5%
Common 67
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 93
 
11.3%
e 93
 
11.3%
l 70
 
8.5%
n 66
 
8.0%
i 57
 
6.9%
r 53
 
6.4%
o 43
 
5.2%
s 41
 
5.0%
t 29
 
3.5%
d 25
 
3.0%
Other values (39) 253
30.7%
Common
ValueCountFrequency (%)
66
98.5%
- 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 888
99.8%
None 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 93
 
10.5%
e 93
 
10.5%
l 70
 
7.9%
n 66
 
7.4%
66
 
7.4%
i 57
 
6.4%
r 53
 
6.0%
o 43
 
4.8%
s 41
 
4.6%
t 29
 
3.3%
Other values (39) 277
31.2%
None
ValueCountFrequency (%)
ø 1
50.0%
ð 1
50.0%

Affiliate
Text

MISSING 

Distinct54
Distinct (%)94.7%
Missing3
Missing (%)5.0%
Memory size960.0 B
2024-02-17T21:59:58.359032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length37
Median length22
Mean length16.754386
Min length11

Characters and Unicode

Total characters955
Distinct characters56
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)89.5%

Sample

1st rowCrossFit Urban Energy
2nd rowCrossFit 4E
3rd rowReebok CrossFit Frankston
4th rowCrossFit Yas
5th rowCrossFit Mayhem
ValueCountFrequency (%)
crossfit 57
44.9%
ttt 2
 
1.6%
england 2
 
1.6%
invictus 2
 
1.6%
new 2
 
1.6%
yas 2
 
1.6%
sport 1
 
0.8%
reebok 1
 
0.8%
frankston 1
 
0.8%
colosseum 1
 
0.8%
Other values (56) 56
44.1%
2024-02-17T21:59:58.636571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 133
13.9%
i 87
 
9.1%
o 80
 
8.4%
t 80
 
8.4%
r 79
 
8.3%
70
 
7.3%
C 65
 
6.8%
F 60
 
6.3%
e 34
 
3.6%
a 31
 
3.2%
Other values (46) 236
24.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 677
70.9%
Uppercase Letter 195
 
20.4%
Space Separator 70
 
7.3%
Decimal Number 13
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 133
19.6%
i 87
12.9%
o 80
11.8%
t 80
11.8%
r 79
11.7%
e 34
 
5.0%
a 31
 
4.6%
n 24
 
3.5%
l 21
 
3.1%
y 14
 
2.1%
Other values (16) 94
13.9%
Uppercase Letter
ValueCountFrequency (%)
C 65
33.3%
F 60
30.8%
E 9
 
4.6%
N 7
 
3.6%
S 7
 
3.6%
T 7
 
3.6%
B 6
 
3.1%
H 4
 
2.1%
P 4
 
2.1%
R 4
 
2.1%
Other values (13) 22
 
11.3%
Decimal Number
ValueCountFrequency (%)
4 4
30.8%
2 3
23.1%
9 2
15.4%
1 2
15.4%
6 1
 
7.7%
3 1
 
7.7%
Space Separator
ValueCountFrequency (%)
70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 872
91.3%
Common 83
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 133
15.3%
i 87
10.0%
o 80
9.2%
t 80
9.2%
r 79
 
9.1%
C 65
 
7.5%
F 60
 
6.9%
e 34
 
3.9%
a 31
 
3.6%
n 24
 
2.8%
Other values (39) 199
22.8%
Common
ValueCountFrequency (%)
70
84.3%
4 4
 
4.8%
2 3
 
3.6%
9 2
 
2.4%
1 2
 
2.4%
6 1
 
1.2%
3 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 954
99.9%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 133
13.9%
i 87
 
9.1%
o 80
 
8.4%
t 80
 
8.4%
r 79
 
8.3%
70
 
7.3%
C 65
 
6.8%
F 60
 
6.3%
e 34
 
3.6%
a 31
 
3.2%
Other values (45) 235
24.6%
None
ValueCountFrequency (%)
í 1
100.0%

Country
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing60
Missing (%)100.0%
Memory size960.0 B

Region
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size960.0 B
worldwide
60 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters540
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowworldwide
2nd rowworldwide
3rd rowworldwide
4th rowworldwide
5th rowworldwide

Common Values

ValueCountFrequency (%)
worldwide 60
100.0%

Length

2024-02-17T21:59:58.778240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:59:58.858879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
worldwide 60
100.0%

Most occurring characters

ValueCountFrequency (%)
w 120
22.2%
d 120
22.2%
o 60
11.1%
r 60
11.1%
l 60
11.1%
i 60
11.1%
e 60
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 540
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 120
22.2%
d 120
22.2%
o 60
11.1%
r 60
11.1%
l 60
11.1%
i 60
11.1%
e 60
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 120
22.2%
d 120
22.2%
o 60
11.1%
r 60
11.1%
l 60
11.1%
i 60
11.1%
e 60
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 120
22.2%
d 120
22.2%
o 60
11.1%
r 60
11.1%
l 60
11.1%
i 60
11.1%
e 60
11.1%

Division
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size960.0 B
Women
60 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWomen
2nd rowWomen
3rd rowWomen
4th rowWomen
5th rowWomen

Common Values

ValueCountFrequency (%)
Women 60
100.0%

Length

2024-02-17T21:59:58.946372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:59:59.029043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
women 60
100.0%

Most occurring characters

ValueCountFrequency (%)
W 60
20.0%
o 60
20.0%
m 60
20.0%
e 60
20.0%
n 60
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 240
80.0%
Uppercase Letter 60
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 60
25.0%
m 60
25.0%
e 60
25.0%
n 60
25.0%
Uppercase Letter
ValueCountFrequency (%)
W 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 300
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 60
20.0%
o 60
20.0%
m 60
20.0%
e 60
20.0%
n 60
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 60
20.0%
o 60
20.0%
m 60
20.0%
e 60
20.0%
n 60
20.0%

Rank
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.85
Minimum4
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T21:59:59.119523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.95
Q115
median26.5
Q343.25
95-th percentile55
Maximum59
Range55
Interquartile range (IQR)28.25

Descriptive statistics

Standard deviation16.002992
Coefficient of variation (CV)0.55469644
Kurtosis-1.2190842
Mean28.85
Median Absolute Deviation (MAD)13
Skewness0.23782251
Sum1731
Variance256.09576
MonotonicityIncreasing
2024-02-17T21:59:59.232960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
21 3
 
5.0%
12 3
 
5.0%
27 3
 
5.0%
15 3
 
5.0%
55 2
 
3.3%
7 2
 
3.3%
13 2
 
3.3%
37 2
 
3.3%
16 2
 
3.3%
17 2
 
3.3%
Other values (31) 36
60.0%
ValueCountFrequency (%)
4 1
 
1.7%
5 2
3.3%
6 1
 
1.7%
7 2
3.3%
11 1
 
1.7%
12 3
5.0%
13 2
3.3%
14 1
 
1.7%
15 3
5.0%
16 2
3.3%
ValueCountFrequency (%)
59 1
1.7%
56 1
1.7%
55 2
3.3%
53 1
1.7%
52 1
1.7%
51 1
1.7%
50 2
3.3%
49 1
1.7%
48 1
1.7%
46 2
3.3%

Games_Level
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size960.0 B
worldwide
60 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters540
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowworldwide
2nd rowworldwide
3rd rowworldwide
4th rowworldwide
5th rowworldwide

Common Values

ValueCountFrequency (%)
worldwide 60
100.0%

Length

2024-02-17T21:59:59.342249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:59:59.421799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
worldwide 60
100.0%

Most occurring characters

ValueCountFrequency (%)
w 120
22.2%
d 120
22.2%
o 60
11.1%
r 60
11.1%
l 60
11.1%
i 60
11.1%
e 60
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 540
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 120
22.2%
d 120
22.2%
o 60
11.1%
r 60
11.1%
l 60
11.1%
i 60
11.1%
e 60
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 120
22.2%
d 120
22.2%
o 60
11.1%
r 60
11.1%
l 60
11.1%
i 60
11.1%
e 60
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 120
22.2%
d 120
22.2%
o 60
11.1%
r 60
11.1%
l 60
11.1%
i 60
11.1%
e 60
11.1%

Qualifier
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size960.0 B
semifinals
60 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters600
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsemifinals
2nd rowsemifinals
3rd rowsemifinals
4th rowsemifinals
5th rowsemifinals

Common Values

ValueCountFrequency (%)
semifinals 60
100.0%

Length

2024-02-17T21:59:59.508585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:59:59.588353image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
semifinals 60
100.0%

Most occurring characters

ValueCountFrequency (%)
s 120
20.0%
i 120
20.0%
e 60
10.0%
m 60
10.0%
f 60
10.0%
n 60
10.0%
a 60
10.0%
l 60
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 600
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 120
20.0%
i 120
20.0%
e 60
10.0%
m 60
10.0%
f 60
10.0%
n 60
10.0%
a 60
10.0%
l 60
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 120
20.0%
i 120
20.0%
e 60
10.0%
m 60
10.0%
f 60
10.0%
n 60
10.0%
a 60
10.0%
l 60
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 120
20.0%
i 120
20.0%
e 60
10.0%
m 60
10.0%
f 60
10.0%
n 60
10.0%
a 60
10.0%
l 60
10.0%

Back Squat (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)57.4%
Missing6
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean290.62906
Minimum220
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T21:59:59.671628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile238.13033
Q1266.25
median296.31185
Q3313.75
95-th percentile338.5
Maximum365
Range145
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation32.906559
Coefficient of variation (CV)0.11322529
Kurtosis-0.54533797
Mean290.62906
Median Absolute Deviation (MAD)23.35805
Skewness-0.11971995
Sum15693.969
Variance1082.8416
MonotonicityNot monotonic
2024-02-17T21:59:59.781279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
300 5
 
8.3%
310 3
 
5.0%
275 3
 
5.0%
250 3
 
5.0%
319.6699 2
 
3.3%
242.5082 2
 
3.3%
320 2
 
3.3%
265 2
 
3.3%
305 2
 
3.3%
275.5775 2
 
3.3%
Other values (21) 28
46.7%
(Missing) 6
 
10.0%
ValueCountFrequency (%)
220 1
 
1.7%
230 2
3.3%
242.5082 2
3.3%
249.12206 1
 
1.7%
250 3
5.0%
253.5313 1
 
1.7%
260.14516 2
3.3%
265 2
3.3%
270 2
3.3%
275 3
5.0%
ValueCountFrequency (%)
365 1
1.7%
346.12534 1
1.7%
345 1
1.7%
335 2
3.3%
330 2
3.3%
325 1
1.7%
320 2
3.3%
319.6699 2
3.3%
315.26066 1
1.7%
315 1
1.7%

Clean and Jerk (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)66.7%
Missing3
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean224.13512
Minimum165
Maximum269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T21:59:59.900333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum165
5-th percentile179.89699
Q1210
median230
Q3238.09896
95-th percentile255
Maximum269
Range104
Interquartile range (IQR)28.09896

Descriptive statistics

Standard deviation23.143767
Coefficient of variation (CV)0.1032581
Kurtosis0.011822386
Mean224.13512
Median Absolute Deviation (MAD)13
Skewness-0.61738327
Sum12775.702
Variance535.63396
MonotonicityNot monotonic
2024-02-17T22:00:00.023039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
235 4
 
6.7%
225 4
 
6.7%
230 4
 
6.7%
231.4851 3
 
5.0%
255 3
 
5.0%
220 3
 
5.0%
205.02966 2
 
3.3%
238.09896 2
 
3.3%
250 2
 
3.3%
210 2
 
3.3%
Other values (28) 28
46.7%
(Missing) 3
 
5.0%
ValueCountFrequency (%)
165 1
1.7%
175 1
1.7%
176.3696 1
1.7%
180.77884 1
1.7%
182.98346 1
1.7%
185 1
1.7%
190 1
1.7%
198.4158 1
1.7%
200 1
1.7%
202.82504 1
1.7%
ValueCountFrequency (%)
269 1
 
1.7%
260.14516 1
 
1.7%
255 3
5.0%
253.5313 1
 
1.7%
250 2
3.3%
245 1
 
1.7%
244.71282 1
 
1.7%
243 1
 
1.7%
242.5082 1
 
1.7%
242 1
 
1.7%

Deadlift (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)65.5%
Missing5
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean342.8457
Minimum220
Maximum440.924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T22:00:00.139701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile286.6006
Q1317.83495
median341.7161
Q3360.88115
95-th percentile405.85641
Maximum440.924
Range220.924
Interquartile range (IQR)43.0462

Descriptive statistics

Standard deviation41.845737
Coefficient of variation (CV)0.12205414
Kurtosis0.57382467
Mean342.8457
Median Absolute Deviation (MAD)22.0462
Skewness0.032259342
Sum18856.514
Variance1751.0657
MonotonicityNot monotonic
2024-02-17T22:00:00.250877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
325 4
 
6.7%
350 3
 
5.0%
330 3
 
5.0%
405 3
 
5.0%
286.6006 3
 
5.0%
352.7392 3
 
5.0%
400 3
 
5.0%
341.7161 2
 
3.3%
297.6237 2
 
3.3%
355 2
 
3.3%
Other values (26) 27
45.0%
(Missing) 5
 
8.3%
ValueCountFrequency (%)
220 1
 
1.7%
285 1
 
1.7%
286.6006 3
5.0%
290 1
 
1.7%
295 1
 
1.7%
297.6237 2
3.3%
300 1
 
1.7%
305 1
 
1.7%
313.05604 1
 
1.7%
315 1
 
1.7%
ValueCountFrequency (%)
440.924 1
 
1.7%
432 1
 
1.7%
407.8547 1
 
1.7%
405 3
5.0%
400 3
5.0%
385.8085 1
 
1.7%
380 1
 
1.7%
370 1
 
1.7%
365 1
 
1.7%
363.7623 1
 
1.7%

Snatch (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)51.8%
Missing4
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean178.62204
Minimum63
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T22:00:00.350404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile144.57507
Q1170
median182.98346
Q3192.35145
95-th percentile202.21532
Maximum215
Range152
Interquartile range (IQR)22.351455

Descriptive statistics

Standard deviation23.177927
Coefficient of variation (CV)0.12975962
Kurtosis10.355171
Mean178.62204
Median Absolute Deviation (MAD)12.01654
Skewness-2.3837032
Sum10002.834
Variance537.21632
MonotonicityNot monotonic
2024-02-17T22:00:00.462490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
185 7
 
11.7%
180 5
 
8.3%
170 4
 
6.7%
190 3
 
5.0%
195 3
 
5.0%
176.3696 3
 
5.0%
200 2
 
3.3%
175 2
 
3.3%
198.4158 2
 
3.3%
169.75574 2
 
3.3%
Other values (19) 23
38.3%
(Missing) 4
 
6.7%
ValueCountFrequency (%)
63 1
1.7%
135 1
1.7%
143.3003 1
1.7%
145 1
1.7%
150 1
1.7%
155 1
1.7%
156.52802 1
1.7%
158.73264 1
1.7%
160.93726 1
1.7%
165.3465 2
3.3%
ValueCountFrequency (%)
215 2
3.3%
207 1
 
1.7%
200.62042 1
 
1.7%
200 2
3.3%
198.4158 2
3.3%
198 1
 
1.7%
197 1
 
1.7%
195 3
5.0%
194 1
 
1.7%
191.80194 2
3.3%

Fight Gone Bad
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing52
Missing (%)86.7%
Infinite0
Infinite (%)0.0%
Mean394
Minimum346
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T22:00:00.563050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum346
5-th percentile346.7
Q1369.75
median388
Q3405.25
95-th percentile459.45
Maximum464
Range118
Interquartile range (IQR)35.5

Descriptive statistics

Standard deviation43.008305
Coefficient of variation (CV)0.10915813
Kurtosis-0.44687371
Mean394
Median Absolute Deviation (MAD)25.5
Skewness0.75378247
Sum3152
Variance1849.7143
MonotonicityNot monotonic
2024-02-17T22:00:00.879054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
464 1
 
1.7%
389 1
 
1.7%
451 1
 
1.7%
377 1
 
1.7%
346 1
 
1.7%
387 1
 
1.7%
390 1
 
1.7%
348 1
 
1.7%
(Missing) 52
86.7%
ValueCountFrequency (%)
346 1
1.7%
348 1
1.7%
377 1
1.7%
387 1
1.7%
389 1
1.7%
390 1
1.7%
451 1
1.7%
464 1
1.7%
ValueCountFrequency (%)
464 1
1.7%
451 1
1.7%
390 1
1.7%
389 1
1.7%
387 1
1.7%
377 1
1.7%
348 1
1.7%
346 1
1.7%

Max Pull-ups
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)55.0%
Missing40
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean49.6
Minimum30
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T22:00:00.964350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q139.75
median50
Q360
95-th percentile62.25
Maximum86
Range56
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation13.311649
Coefficient of variation (CV)0.26838002
Kurtosis1.6401133
Mean49.6
Median Absolute Deviation (MAD)10
Skewness0.74988892
Sum992
Variance177.2
MonotonicityNot monotonic
2024-02-17T22:00:01.055947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
50 6
 
10.0%
60 3
 
5.0%
30 2
 
3.3%
61 2
 
3.3%
36 1
 
1.7%
86 1
 
1.7%
45 1
 
1.7%
33 1
 
1.7%
39 1
 
1.7%
40 1
 
1.7%
(Missing) 40
66.7%
ValueCountFrequency (%)
30 2
 
3.3%
33 1
 
1.7%
36 1
 
1.7%
39 1
 
1.7%
40 1
 
1.7%
45 1
 
1.7%
50 6
10.0%
51 1
 
1.7%
60 3
5.0%
61 2
 
3.3%
ValueCountFrequency (%)
86 1
 
1.7%
61 2
 
3.3%
60 3
5.0%
51 1
 
1.7%
50 6
10.0%
45 1
 
1.7%
40 1
 
1.7%
39 1
 
1.7%
36 1
 
1.7%
33 1
 
1.7%

Chad1000x (s)
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing59
Missing (%)98.3%
Memory size960.0 B
3549.0

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row3549.0

Common Values

ValueCountFrequency (%)
3549.0 1
 
1.7%
(Missing) 59
98.3%

Length

2024-02-17T22:00:01.157515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T22:00:01.235400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
3549.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
3 1
16.7%
5 1
16.7%
4 1
16.7%
9 1
16.7%
. 1
16.7%
0 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5
83.3%
Other Punctuation 1
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1
20.0%
5 1
20.0%
4 1
20.0%
9 1
20.0%
0 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1
16.7%
5 1
16.7%
4 1
16.7%
9 1
16.7%
. 1
16.7%
0 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1
16.7%
5 1
16.7%
4 1
16.7%
9 1
16.7%
. 1
16.7%
0 1
16.7%

L1 Benchmark (s)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing60
Missing (%)100.0%
Memory size960.0 B

Filthy 50 (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing54
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean1233
Minimum1054
Maximum1410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T22:00:01.307151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1054
5-th percentile1061.25
Q11096.25
median1235.5
Q31368.75
95-th percentile1402.5
Maximum1410
Range356
Interquartile range (IQR)272.5

Descriptive statistics

Standard deviation159.55689
Coefficient of variation (CV)0.12940542
Kurtosis-2.817553
Mean1233
Median Absolute Deviation (MAD)148.5
Skewness-0.017728528
Sum7398
Variance25458.4
MonotonicityNot monotonic
2024-02-17T22:00:01.399358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1410 1
 
1.7%
1136 1
 
1.7%
1380 1
 
1.7%
1083 1
 
1.7%
1054 1
 
1.7%
1335 1
 
1.7%
(Missing) 54
90.0%
ValueCountFrequency (%)
1054 1
1.7%
1083 1
1.7%
1136 1
1.7%
1335 1
1.7%
1380 1
1.7%
1410 1
1.7%
ValueCountFrequency (%)
1410 1
1.7%
1380 1
1.7%
1335 1
1.7%
1136 1
1.7%
1083 1
1.7%
1054 1
1.7%

Fran (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)85.7%
Missing25
Missing (%)41.7%
Infinite0
Infinite (%)0.0%
Mean161.31429
Minimum118
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T22:00:01.505869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum118
5-th percentile119
Q1137
median149
Q3171.5
95-th percentile260.1
Maximum320
Range202
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation44.882704
Coefficient of variation (CV)0.27823143
Kurtosis5.0021455
Mean161.31429
Median Absolute Deviation (MAD)19
Skewness2.1424626
Sum5646
Variance2014.4571
MonotonicityNot monotonic
2024-02-17T22:00:01.624302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
143 3
 
5.0%
149 2
 
3.3%
119 2
 
3.3%
137 2
 
3.3%
141 1
 
1.7%
193 1
 
1.7%
174 1
 
1.7%
124 1
 
1.7%
138 1
 
1.7%
195 1
 
1.7%
Other values (20) 20
33.3%
(Missing) 25
41.7%
ValueCountFrequency (%)
118 1
1.7%
119 2
3.3%
122 1
1.7%
124 1
1.7%
126 1
1.7%
130 1
1.7%
135 1
1.7%
137 2
3.3%
138 1
1.7%
139 1
1.7%
ValueCountFrequency (%)
320 1
1.7%
286 1
1.7%
249 1
1.7%
197 1
1.7%
195 1
1.7%
193 1
1.7%
181 1
1.7%
176 1
1.7%
174 1
1.7%
169 1
1.7%

Grace (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)89.7%
Missing31
Missing (%)51.7%
Infinite0
Infinite (%)0.0%
Mean121
Minimum69
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T22:00:01.734623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum69
5-th percentile74.6
Q194
median114
Q3132
95-th percentile202
Maximum273
Range204
Interquartile range (IQR)38

Descriptive statistics

Standard deviation44.630227
Coefficient of variation (CV)0.36884485
Kurtosis4.1468806
Mean121
Median Absolute Deviation (MAD)20
Skewness1.7888901
Sum3509
Variance1991.8571
MonotonicityNot monotonic
2024-02-17T22:00:01.847776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
150 2
 
3.3%
117 2
 
3.3%
112 2
 
3.3%
86 1
 
1.7%
175 1
 
1.7%
79 1
 
1.7%
81 1
 
1.7%
118 1
 
1.7%
220 1
 
1.7%
104 1
 
1.7%
Other values (16) 16
26.7%
(Missing) 31
51.7%
ValueCountFrequency (%)
69 1
1.7%
73 1
1.7%
77 1
1.7%
79 1
1.7%
80 1
1.7%
81 1
1.7%
86 1
1.7%
94 1
1.7%
95 1
1.7%
98 1
1.7%
ValueCountFrequency (%)
273 1
1.7%
220 1
1.7%
175 1
1.7%
167 1
1.7%
150 2
3.3%
134 1
1.7%
132 1
1.7%
128 1
1.7%
123 1
1.7%
121 1
1.7%

Helen (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing50
Missing (%)83.3%
Infinite0
Infinite (%)0.0%
Mean590.3
Minimum471
Maximum768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T22:00:01.945325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum471
5-th percentile477.75
Q1514.75
median556.5
Q3651
95-th percentile750.9
Maximum768
Range297
Interquartile range (IQR)136.25

Descriptive statistics

Standard deviation103.52997
Coefficient of variation (CV)0.17538535
Kurtosis-0.94546532
Mean590.3
Median Absolute Deviation (MAD)78
Skewness0.63227647
Sum5903
Variance10718.456
MonotonicityNot monotonic
2024-02-17T22:00:02.033534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
652 1
 
1.7%
768 1
 
1.7%
512 1
 
1.7%
566 1
 
1.7%
648 1
 
1.7%
523 1
 
1.7%
486 1
 
1.7%
730 1
 
1.7%
547 1
 
1.7%
471 1
 
1.7%
(Missing) 50
83.3%
ValueCountFrequency (%)
471 1
1.7%
486 1
1.7%
512 1
1.7%
523 1
1.7%
547 1
1.7%
566 1
1.7%
648 1
1.7%
652 1
1.7%
730 1
1.7%
768 1
1.7%
ValueCountFrequency (%)
768 1
1.7%
730 1
1.7%
652 1
1.7%
648 1
1.7%
566 1
1.7%
547 1
1.7%
523 1
1.7%
512 1
1.7%
486 1
1.7%
471 1
1.7%

Run 5k (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)100.0%
Missing45
Missing (%)75.0%
Infinite0
Infinite (%)0.0%
Mean1340.4667
Minimum1200
Maximum1525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T22:00:02.125634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1219.6
Q11273
median1340
Q31391.5
95-th percentile1520.8
Maximum1525
Range325
Interquartile range (IQR)118.5

Descriptive statistics

Standard deviation98.732587
Coefficient of variation (CV)0.073655384
Kurtosis-0.28930639
Mean1340.4667
Median Absolute Deviation (MAD)69
Skewness0.56830776
Sum20107
Variance9748.1238
MonotonicityNot monotonic
2024-02-17T22:00:02.225164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1260 1
 
1.7%
1294 1
 
1.7%
1286 1
 
1.7%
1352 1
 
1.7%
1519 1
 
1.7%
1351 1
 
1.7%
1374 1
 
1.7%
1228 1
 
1.7%
1200 1
 
1.7%
1409 1
 
1.7%
Other values (5) 5
 
8.3%
(Missing) 45
75.0%
ValueCountFrequency (%)
1200 1
1.7%
1228 1
1.7%
1230 1
1.7%
1260 1
1.7%
1286 1
1.7%
1294 1
1.7%
1312 1
1.7%
1340 1
1.7%
1351 1
1.7%
1352 1
1.7%
ValueCountFrequency (%)
1525 1
1.7%
1519 1
1.7%
1427 1
1.7%
1409 1
1.7%
1374 1
1.7%
1352 1
1.7%
1351 1
1.7%
1340 1
1.7%
1312 1
1.7%
1294 1
1.7%

Sprint 400m (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)85.7%
Missing46
Missing (%)76.7%
Infinite0
Infinite (%)0.0%
Mean71.357143
Minimum59
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-02-17T22:00:02.324430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile59.65
Q162
median70.5
Q379.5
95-th percentile87.35
Maximum88
Range29
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation10.058346
Coefficient of variation (CV)0.14095781
Kurtosis-1.2064655
Mean71.357143
Median Absolute Deviation (MAD)9.5
Skewness0.32535416
Sum999
Variance101.17033
MonotonicityNot monotonic
2024-02-17T22:00:02.413555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
80 2
 
3.3%
60 2
 
3.3%
66 1
 
1.7%
61 1
 
1.7%
88 1
 
1.7%
69 1
 
1.7%
59 1
 
1.7%
87 1
 
1.7%
72 1
 
1.7%
74 1
 
1.7%
Other values (2) 2
 
3.3%
(Missing) 46
76.7%
ValueCountFrequency (%)
59 1
1.7%
60 2
3.3%
61 1
1.7%
65 1
1.7%
66 1
1.7%
69 1
1.7%
72 1
1.7%
74 1
1.7%
78 1
1.7%
80 2
3.3%
ValueCountFrequency (%)
88 1
1.7%
87 1
1.7%
80 2
3.3%
78 1
1.7%
74 1
1.7%
72 1
1.7%
69 1
1.7%
66 1
1.7%
65 1
1.7%
61 1
1.7%

Interactions

2024-02-17T21:59:56.222308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:42.783695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.814581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:44.876471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:45.962912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:47.710214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.722165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:49.840296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:50.975655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.965056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.992198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:54.047389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.249206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:56.304371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:42.875489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.890135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:44.954113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:46.035816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:47.788229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.800844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:49.909271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.052763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.043147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:53.071230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:54.114775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.323304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:56.377981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:42.957017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.973368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:45.036560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:46.116369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:47.870089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.883777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:49.985650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.126040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.128536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:53.155296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:54.195335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.392769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:56.458030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.030818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:44.052500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:45.110073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:46.188849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:47.946747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.965948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:50.066455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.208088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.207253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:53.232124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:54.267895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.464621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:56.531600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.100466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:44.128245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:45.185076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:46.991131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.022104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:49.042197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:50.142941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.284202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.281814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:53.322750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:54.341404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.540709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:56.600816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.183022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:44.213082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:45.267321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:47.081479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.108591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:49.116364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:50.212382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.361539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.365449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:53.409395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:54.419899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.615216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:56.672541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.262549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:44.291671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:45.346171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:47.161173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.181151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:49.191001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:50.288109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.434569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.443439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:53.487387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:54.503780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.685112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:56.739150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.330849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:44.366640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:45.422118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:47.237659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.251927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:49.292903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:50.358457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.508982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.519363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:53.567101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:54.573034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.765942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:56.812138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.415075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:44.448614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:45.505257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:47.321882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.332679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:49.382406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:50.595259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.591455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.597097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:53.647959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:54.858973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.833963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:56.882283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.503683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:44.558229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:45.599519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:47.401679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.412371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:49.484288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:50.675009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.663984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.681953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:53.733393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:54.940274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.917526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:56.958483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.587204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:44.648321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:45.693467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:47.489902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.498703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:49.583180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:50.756186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.745596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.765050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:53.819745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.025008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.992364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:57.039451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.660162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:44.727040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:45.789515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:47.561512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.574316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:49.675779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:50.823978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.823554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.839291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:53.896585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.094854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:56.071232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:57.115479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:43.735683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:44.802332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:45.879994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:47.638133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:48.651506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:49.756575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:50.909006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:51.891744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:52.920161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:53.973235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:55.170652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:59:56.147968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-17T22:00:02.492927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Back Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Fight Gone BadFilthy 50 (s)Fran (s)Grace (s)Helen (s)Max Pull-upsRankRun 5k (s)Snatch (lbs)Sprint 400m (s)
Back Squat (lbs)1.0000.7820.6640.429-0.200-0.388-0.475-0.225-0.059-0.085-0.3200.593-0.149
Clean and Jerk (lbs)0.7821.0000.669-0.157-0.429-0.426-0.364-0.4910.099-0.1310.0560.7800.073
Deadlift (lbs)0.6640.6691.0000.024-0.371-0.394-0.578-0.321-0.176-0.0840.2400.4800.043
Fight Gone Bad0.429-0.1570.0241.000-1.000-0.321-0.2141.000-0.029-0.539-1.000-0.085-0.500
Filthy 50 (s)-0.200-0.429-0.371-1.0001.0000.257-1.0000.8000.866-0.5431.0000.1430.500
Fran (s)-0.388-0.426-0.394-0.3210.2571.0000.3870.500-0.263-0.1220.249-0.3020.536
Grace (s)-0.475-0.364-0.578-0.214-1.0000.3871.0000.429-0.372-0.007-0.044-0.3610.119
Helen (s)-0.225-0.491-0.3211.0000.8000.5000.4291.000-0.667-0.5030.829-0.1331.000
Max Pull-ups-0.0590.099-0.176-0.0290.866-0.263-0.372-0.6671.0000.1050.0750.1050.472
Rank-0.085-0.131-0.084-0.539-0.543-0.122-0.007-0.5030.1051.0000.168-0.021-0.237
Run 5k (s)-0.3200.0560.240-1.0001.0000.249-0.0440.8290.0750.1681.000-0.0450.901
Snatch (lbs)0.5930.7800.480-0.0850.143-0.302-0.361-0.1330.105-0.021-0.0451.000-0.022
Sprint 400m (s)-0.1490.0730.043-0.5000.5000.5360.1191.0000.472-0.2370.901-0.0221.000

Missing values

2024-02-17T21:59:57.246925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-17T21:59:57.510784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
74742Katelin Van ZylCrossFit Urban EnergyNaNworldwideWomen4.0worldwidesemifinals319.66990231.48510385.80850182.98346NaNNaNNaNNaNNaN164.086.0NaN1260.0NaN
74743Tanha BouffeCrossFit 4ENaNworldwideWomen5.0worldwidesemifinals275.57750205.02966286.60060169.75574NaNNaNNaNNaN1410.0249.0NaN652.0NaNNaN
74744Madeline SturtReebok CrossFit FrankstonNaNworldwideWomen5.0worldwidesemifinals308.64680244.71282407.85470165.34650NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
74745Gemma RaderCrossFit YasNaNworldwideWomen6.0worldwidesemifinals275.00000NaN330.00000190.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
74748Luiza MarquesCrossFit MayhemNaNworldwideWomen7.0worldwidesemifinals230.00000175.00000220.00000145.00000NaNNaNNaNNaNNaN286.0NaNNaNNaNNaN
74749Emma Holliday24N46E CrossFitNaNworldwideWomen7.0worldwidesemifinals260.14516176.36960313.05604143.30030NaNNaNNaNNaNNaNNaN273.0768.0NaNNaN
74751Milana YakovlevaCrossFit UdarnikNaNworldwideWomen11.0worldwidesemifinals249.12206198.41580297.62370169.75574NaN50.0NaNNaNNaN147.0134.0NaNNaN80.0
74753Kloie WilsonCrossFit DixieNaNworldwideWomen12.0worldwidesemifinals300.00000225.00000355.00000197.00000NaN30.0NaNNaNNaNNaN98.0NaN1294.0NaN
74754Jacqueline DahlstrømC23 CrossFitNaNworldwideWomen12.0worldwidesemifinals286.60060231.48510330.69300182.98346NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
74755Madeline ShellingCrossFit SelwynNaNworldwideWomen12.0worldwidesemifinalsNaN233.68972352.73920NaNNaNNaNNaNNaNNaN143.0150.0NaNNaNNaN
AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
74811Ashley WaldenCrossFit BladeNaNworldwideWomen49.0worldwidesemifinals250.0000225.000340.0000185.0000387.061.0NaNNaNNaN174.0104.0471.01312.0NaN
74812Jeromine GeroudetCrossFit LazuliNaNworldwideWomen50.0worldwidesemifinals286.6006220.462341.7161165.3465390.030.0NaNNaN1054.0193.0220.0NaNNaNNaN
74813Alina WardCrossFit SpragNaNworldwideWomen50.0worldwidesemifinals310.0000220.000400.0000180.0000NaN39.0NaNNaNNaNNaN118.0NaN1230.060.0
74814Carlie StoneNaNNaNworldwideWomen51.0worldwidesemifinals290.0000230.000400.0000180.0000348.040.0NaNNaNNaN137.081.0NaN1427.065.0
74816Stephanie MirelesCrossFit Invictus Back BayNaNworldwideWomen52.0worldwidesemifinals250.0000230.000315.0000200.0000NaNNaNNaNNaNNaN143.0NaNNaNNaNNaN
74817Carrie StevensonCrossFit i1uvitNaNworldwideWomen53.0worldwidesemifinals270.0000220.000325.0000185.0000NaN51.0NaNNaNNaN141.0NaNNaNNaNNaN
74819Bailey BentleyCrossFit HuntsvilleNaNworldwideWomen55.0worldwidesemifinals320.0000230.000370.0000175.0000NaN61.0NaNNaNNaN118.079.0NaNNaNNaN
74820Natalie JenningsCrossFit UpNaNworldwideWomen55.0worldwidesemifinalsNaN210.000NaN170.0000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
74821Rachel FrickerCrossFit ExplodeNaNworldwideWomen56.0worldwidesemifinals300.0000225.000335.0000180.0000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
74822Madeline HelmsCrossFit Electric CityNaNworldwideWomen59.0worldwidesemifinals295.0000210.000325.0000155.0000NaNNaNNaNNaN1335.0152.0114.0NaN1340.0NaN